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Alcohol use during adolescence represents a major health concern given that this is a period in which the brain continues to undergo critical developmental changes. Much behavioral research has been conducted in animal models of alcohol exposure and a vulnerable period in adolescence has been identified that suggests lasting effects of ethanol exposure during adolescence. However, identification of molecular changes underlying the behavioral outcomes observed as a result from exposure to ethanol during adolescence remains a major technical challenge. In this chapter, we describe a method that allows for assessment of the effects of chronic ethanol exposure during adolescence relative to adulthood through global-scale analysis of protein expression as well as evaluation of behavioral responsivity in adolescent and adult rats. Results from this type of analysis can facilitate identification of age-specific molecular markers associated with behavioral changes following treatment with ethanol or in other animal models of drug abuse.
Adolescence is a unique time period during which individuals typically experiment with alcohol and consequently are at a greater predisposition to develop alcohol dependency. This developmental period is important since studies show that adults with substance abuse disorders initiate alcohol and drug use in adolescence, a period of considerable brain growth (1). Furthermore, the use of alcohol early in life is a critical predictor of abuse liability later in life for humans (2,3). To date, little research has focused on understanding the molecular mechanisms of alcohol’s effects in the developing animal, and even less on the effects of adolescent ethanol exposure and subsequent adult responses. The need for an adolescent animal model of alcohol abuse that examines both behavioral changes and molecular changes associated with alcohol has been raised (4,5). Thus, it is critical to examine the lasting behavioral and molecular impact of ethanol exposure during development of brain reward mechanisms and on subsequent function.
Adolescents differ from adults in ethanol-induced behavioral responsivity, underscoring the importance of studying adolescence. For example, adolescent animals consume more ethanol than adults (6) and ethanol intake levels are high throughout adolescence and decrease to levels slightly higher than adult comparisons (7). Additionally, ethanol exposure during adolescence potentiates subsequent preference for ethanol in adult mice (8) and developing animals differ from adults in pharmacological sensitivity to ethanol (5) with preweanling rats differing from other ages in ethanol tolerance (9). Adolescent rats develop an ethanol-induced place preference more readily than adult animals (10) and showed a heightened preference for novelty (11). In this chapter, an approach to determine potential age-specific ethanol-induced changes in behavior is described.
In order to determine the molecular mechanisms underlying age-specific behavioral changes observed in an animal model after chronic ethanol exposure, proteomics-based analysis can be employed to provide an unbiased global scale assessment of ethanol-induced neuroprotein differential expression. For example, changes in various high abundance proteins in adolescent rat hippocampus have been observed after chronic alcohol exposure using proteomic analysis by 2D gel electrophoresis (12). A mass spectrometry-based relative protein quantitation approach for the proteomic analysis of brain tissue in an in vivo model of chronic ethanol exposure is described here and is an effective methodology that can complement other proteomics-based techniques such as 2D gel electrophoresis, ultimately to provide a molecular link at the protein level to various age-specific alcohol-induced behavioral outcomes. This approach incorporates either a “label-free” or a chemical tagging method using isobaric tags for relative and absolute quantitation (iTRAQ) (13) depending on the instrumentation that is available for mass spectrometry analysis.
Dilute ethanol from a 95% stock solution (Pharmaco-Aaper, Shelbyville, KY) to 17% v/v in saline (0.9 % NaCl). The vehicle is an isovolumetric administration of saline. Both ethanol and saline are intraperitoneally administered as a 1.5 g/kg dose. This is achieved by multiplying the weight of the animal by 0.01117.
Major instrumentation for the proteomic analysis of rat brain tissue includes a HPLC system capable of nanoflow rates (250 nl/min) for online reversed-phase HPLC separation of the rat brain protein tryptic digests and a mass spectrometer. For relative protein quantitation by spectral counting, a low resolution mass spectrometer such as a quadrupole ion trap can be used. However, higher resolution is ideal for analysis of iTRAQ-labeled peptides. Specific items used in the proteomic analysis reported in this chapter are listed below.
Several data analysis software packages can be used for processing of mass spectrometric data for relative protein quantitation by either spectral counting or iTRAQ. Standard database search engines for protein identification include Sequest (Thermo Fisher Scientific) and Mascot (Matrix Science). Relative quantitation based on spectral counting as well as iTRAQ is routinely performed in our lab by the program Scaffold (Proteome Software, Portland, OR); however, other commercial packages such as the quantitation toolbox in Mascot Distiller (Matrix Science) and Proteome Discoverer (Thermo Fisher Scientific) can be used for iTRAQ data analysis.
Scaffold allows the user to compare multiple proteomic datasets and sort the list of identified proteins by various parameters including spectral counts. In the spectral counting method, the total number of MS/MS spectra identified for a particular protein is used as a measure of its abundance and consequently this parameter can be used for relative protein quantitation as shown below.
We thank Jean Horak and Jancy Mathew for technical assistance and the Florida Center of Excellence for Biomolecular Identification and Targeted Therapeutics Proteomics Facility for providing access to analytical instrumentation for proteomics analysis.
1The open field employs a black floor to discriminate from the white rat that will be tracked. Depending on the strain of animal to be used, a different color floor may be utilized to enable to Ethovision software to distinguish the animal from the background.
2The open field is enclosed with white curtains through which the ambient lighting can pass through to diffuse the light on either side of the open field and to eliminate spatial cues.
3Several high resolution mass spectrometers (ex., hybrid quadrupole time-of-flight instruments) are commercially available that can provide the appropriate mass resolution and accuracy to carry out iTRAQ-based quantitative proteomics experiments. Data dependent (or information dependent) acquisition parameters shown in this section are for an Orbitrap mass spectrometer and these standard parameters can be optimized accordingly depending on instrument type.
4Ensure that you do not move the barrier when placing the animal back into the arena, as the camera will track the white barrier instead of the white rat for the duration of the trial.
5The time period following the chronic ethanol exposure for tissue harvesting can be altered to determine temporary ethanol-induced protein level changes in addition to long lasting proteomic changes that can occur.
6For this analysis, hippocampus was analyzed given the supporting evidence of age-specific protein level changes that can occur in this particular brain region after ethanol exposure. The dissection of other relevant brain regions (for example, prefrontal cortex, ventral striatum containing the nucleus accumbens, and substantia nigra/ventral tegmental area) requires specific expertise where description of this technique is beyond the scope of this chapter.
7A minimal amount of extraction buffer is required in order to yield a fairly high protein concentration (> 1 mg/ml). This amount will have to be optimized for the weight of brain tissue being analyzed. For the analysis of hippocampus samples, 2 ml of extraction buffer was determined to be sufficient.
8The SDS present in the sample is typically diluted to a level which has low impact on the rpHPLC separation. The SDS concentration can be adjusted to lower levels depending on the subsequent effect on protein solubilization. Alternatively, detergent removal can be performed with commercially available spin columns (ex., detergent removal columns from Pierce Biotechnology).
9If it is necessary to modify buffer components in this protocol, be sure to avoid primary amine-containing reagents since this will affect peptide labeling by the amine-reactive iTRAQ tags.
10There are multiple databases that can be used for the database search. We have used organism-specific International Protein Index databases from the European Bionformatics Institute. Taxonomy specific searches can also be performed with general databases (ex., SwissProt or the NCBI nonredundant database). Sequest and Mascot represent commonly used commercially available search algorithms; however, other open source and public search algorithms are available such as X! Tandem (The Global Proteome Machine Organization) and the Open Mass Spectrometry Search Algorithm (NCBI).
11Certain programs such as Scaffold can determine the normalized spectral count value automatically; therefore, this normalization factor calculation may not be necessary depending on the software package utilized.
12Other statistical tests such as a Fisher’s exact test can be employed to identify statistically significant changes in spectral counts.
13Results should be validated by performing western blots for selected proteins whose expression is modified and at least one whose expression was unaltered. Western blots should be performed on a subsample of the initial cell lysate that was used for proteomic analysis before trypsin digestion.